Associative memories, also referred to as content addressable memories, are widely used in the fields of pattern matching and identification, expert systems and artificial intelligence. A widely used associative memory is the Hopfield artificial neural network. Hopfield artificial neural networks are described, for example, in U.S. Pat. No. 4,660,166 to Hopfield entitled Electronic Network for Collective Decision Based on Large Number of Connections Between Signals.
Associative memories are also described in U.S. Pat. No. 6,581,049 to coinventor Aparicio, IV et al., entitled Artificial Neurons Including Power Series of Weights and Counts That Represent Prior and Next Association, assigned to the assignee of the present application, the disclosure of which is hereby incorporated herein by reference in its entirety as if set forth fully herein. As described in the Abstract of the '049 patent, an artificial neuron includes inputs and dendrites, a respective one of which is associated with a respective one of the inputs. Each dendrite includes a power series of weights, and each weight in a power series includes an associated count for the associated power. The power series of weights preferably is a base-two power series of weights, each weight in the base-two power series including an associated count that represents a bit position. The counts for the associated power preferably are statistical counts. More particularly, the dendrites preferably are sequentially ordered, and the power series of weights preferably includes a pair of first and second power series of weights. Each weight in the first power series includes a first count that is a function of associations of prior dendrites, and each weight of the second power series includes a second count that is a function of associations of next dendrites. More preferably, a first and second power series of weights is provided for each of multiple observation phases. In order to propagate an input signal into the artificial neuron, a trace preferably also is provided that is responsive to an input signal at the associated input. The trace preferably includes a first trace count that is a function of associations of the input signal at prior dendrites, and a second trace count that is a function of associations of the input signal at next dendrites. The first and second power series are responsive to the respective first and second trace counts. The input signal preferably is converted into the first and second trace counts, and a trace wave propagator propagates the respective first and second trace counts into the respective first and second power series of weights.
The last several years of intelligence analysis have seen the shift from search to discovery. Beyond keyword search engines and relational database queries, methods for entity and relationship modeling have emerged to uncover relevant sub-networks of people, places, and things. See, for example, U.S. Patent Application Publication No. 2006/0095653 A1, published May 4, 2006, entitled “Network of Networks of Associative Memory Networks for Knowledge Management” and U.S. Patent Application Publication No. 2005/0163347 A1, published Jul. 28, 2005, entitled “Distance-Based Spatial Representation and Prediction Systems, Methods and Computer Program Products for Associative Memories.” However, challenges related to identification of temporal causality within data may be significant.